Synthetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer

Abstract

Prostate cancer (PCa) is the second most common cancer in men worldwide and the most frequently diagnosed cancer among men in more developed countries. The prognosis of PCa is excellent if detected at an early stage, making early screening crucial for detection and treatment. In recent years, a new form of diffusion magnetic resonance imaging called correlated diffusion imaging (CDI) was introduced, and preliminary results show promise as a screening tool for PCa. In the largest study of its kind, we investigate the relationship between PCa presence and a new variant of CDI we term synthetic correlated diffusion imaging (CDI$$^s$$ s ), as well as its performance for PCa delineation compared to current standard MRI techniques [T2-weighted (T2w) imaging, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging] across a cohort of 200 patient cases. Statistical analyses reveal that hyperintensity in CDI$$^s$$ s is a strong indicator of PCa presence and achieves strong delineation of clinically significant cancerous tissue compared to T2w, DWI, and DCE. These results suggest that CDI$$^s$$ s hyperintensity may be a powerful biomarker for the presence of PCa, and may have a clinical impact as a diagnostic aid for improving PCa screening.

Feasibility of diffusion‐tensor and correlated diffusion imaging for studying white‐matter microstructural abnormalities: Application in COVID‐19

“…show that whereas DTI metrics did not reveal differences between the white matter of COVIDÀ and COVID+ groups, log(CDI) exhibits significant, spatially extensive differences. Compared to the previous instances of CDI applications (so far limited to prostate cancer) (Khalvati et al, 2016;Wong et al, 2013;Wong et al, 2015;Wong et al, 2021) Through simulations and experimental data, we demonstrate that CDI's sensitivity to disease processes is independent of these processing choices. Based on prior literature in prostate cancer (Wong et al, 2013;Wong et al, 2021), a higher log(CDI) can be loosely interpreted as reflecting a denser tissue structure with restricted diffusion.…”

“…We show that whereas DTI metrics did not reveal differences between the white matter of COVID− and COVID+ groups, log(CDI) exhibits significant, spatially extensive differences. Compared to the previous instances of CDI applications (so far limited to prostate cancer) (Khalvati et al, 2016 ; Wong et al, 2013 ; Wong et al, 2015 ; Wong et al, 2021 ), our implementation of CDI involves three key differences: (i) instead of relying on images acquired at multiple gradient strengths, we rely mainly on the multiple diffusion directions, which are part of typical brain DTI acquisitions; (ii) given the small spatial scale of white‐matter structures, we opted to not use a spatial probability distribution function in order to avoid spatial blurring and partial‐volume effects; and (iii) to compress the large dynamic range of brain CDI values and normalize the data distribution, we used log(CDI) in this work.…”

Sensitivity of diffusion-tensor and correlated diffusion imaging to white-matter microstructural abnormalities: application in COVID-19

“…Compared to the previous instances of CDI applications (so far limited to prostate cancer) (Khalvati et al, 2016;Wong et al, 2021Wong et al, , 2015Wong et al, , 2013, our implementation of CDI involves 3 key differences: (i) instead of relying on images acquired at multiple gradient strengths, we rely mainly on the multiple diffusion directions, which are part of typical brain DTI acquisitions; (ii) given the small spatial scale of white-matter structures, we opted to not use a spatial probability distribution function in order to avoid spatial blurring and partial-volume effects; (iii) to compress the large dynamic range of brain CDI values and normalize the data distribution, we used log(CDI) in this work. Through simulations and experimental data, we demonstrate that CDI's sensitivity to disease processes is independent of these processing choices.…”

“…Through simulations and experimental data, we demonstrate that CDI's sensitivity to disease processes is independent of these processing choices. Based on prior literature in prostate cancer (Wong et al, 2021(Wong et al, , 2013, a higher log(CDI) can be loosely interpreted as reflecting a denser tissue structure with restricted diffusion. Interestingly, we find 2 dichotomous trends in log(CDI) of COVID patients: (i) in anterior white matter, patients exhibit lower log(CDI) than controls, suggesting less restricted diffusion in patients; (ii) in the cerebellum, patients exhibit higher log(CDI) than controls, suggesting more restricted diffusion in patients.…”

Skin Cancer Pathobiology at a Glance: A Focus on Imaging Techniques and Their Potential for Improved Diagnosis and Surveillance in Clinical Cohorts

“…At the moment, there are at least three MRI variants available for use in clinical settings, these include: (1) T2-weighted (T2w) imaging, which relies on the assessment of the differences on the T2 relaxation time of tissues; (2) diffusion-weighted imaging (DWI), which involves the calculation of an apparent diffusion coefficient (ADC) from DWI; and (3) dynamic contrast-enhanced (DCE) imaging, which measures T1 changes in tissues over time after the administration of a contrast agent [ 172 ]. The applications of those MRI techniques in dermato-oncology include the assessment of therapeutic responses in CM and SCC [ 173 , 174 ], differentiation of pseudoprogression from progressive disease [ 175 ], and in vivo assessment of tumor angiogenesis for tumor characterization and treatment planning [ 176 ].…”